nf-core/nanostring: a pipeline for reproducible NanoString nCounter analysis.

MOTIVATION: The NanoString™ nCounter® technology platform is a widely used targeted quantification platform for the analysis of gene expression of up to ∼800 genes. Whereas the software tools by the manufacturer can perform the analysis in an interactive and GUI driven approach, there is no portable and user-friendly workflow available that can be used to perform reproducible analysis of multiple samples simultaneously in a scalable fashion on different computing infrastructures. RESULTS: Here, we present the nf-core/nanostring open-source pipeline to perform a comprehensive analysis including quality control and additional features such as expression visualization, annotation with additional metadata and input creation for differential gene expression analysis. The workflow features an easy installation, comprehensive documentation, open-source code with the possibility for further extensions, a strong portability across multiple computing environments and detailed quality metrics reporting covering all parts of the pipeline. nf-core/nanostring has been implemented in the Nextflow workflow language and supports Docker, Singularity, Podman container technologies as well as Conda environments, enabling easy deployment on any Nextflow supported compatible system, including most widely used cloud computing environments such as Google GCP or Amazon AWS. AVAILABILITY AND IMPLEMENTATION: The source code, documentation and installation instructions as well as results for continuous tests are freely available at https://github.com/nf-core/nanostring and https://nf-co.re/nanostring.

Effect of empagliflozin on circulating proteomics in heart failure: mechanistic insights into the EMPEROR programme.

AIMS: Sodium-glucose co-transporter 2 (SGLT2) inhibitors improve cardiovascular outcomes in diverse patient populations, but their mechanism of action requires further study. The aim is to explore the effect of empagliflozin on the circulating levels of intracellular proteins in patients with heart failure, using large-scale proteomics. METHODS AND RESULTS: Over 1250 circulating proteins were measured at baseline, Week 12, and Week 52 in 1134 patients from EMPEROR-Reduced and EMPEROR-Preserved, using the Olink® Explore 1536 platform. Statistical and bioinformatical analyses identified differentially expressed proteins (empagliflozin vs. placebo), which were then linked to demonstrated biological actions in the heart and kidneys. At Week 12, 32 of 1283 proteins fulfilled our threshold for being differentially expressed, i.e. their levels were changed by ≥10% with a false discovery rate <1% (empagliflozin vs. placebo). Among these, nine proteins demonstrated the largest treatment effect of empagliflozin: insulin-like growth factor-binding protein 1, transferrin receptor protein 1, carbonic anhydrase 2, erythropoietin, protein-glutamine gamma-glutamyltransferase 2, thymosin beta-10, U-type mitochondrial creatine kinase, insulin-like growth factor-binding protein 4, and adipocyte fatty acid-binding protein 4. The changes of the proteins from baseline to Week 52 were generally concordant with the changes from the baseline to Week 12, except empagliflozin reduced levels of kidney injury molecule-1 by ≥10% at Week 52, but not at Week 12. The most common biological action of differentially expressed proteins appeared to be the promotion of autophagic flux in the heart, kidney or endothelium, a feature of 6 proteins. Other effects of differentially expressed proteins on the heart included the reduction of oxidative stress, inhibition of inflammation and fibrosis, and the enhancement of mitochondrial health and energy, repair, and regenerative capacity. The actions of differentially expressed proteins in the kidney involved promotion of autophagy, integrity and regeneration, suppression of renal inflammation and fibrosis, and modulation of renal tubular sodium reabsorption. CONCLUSIONS: Changes in circulating protein levels in patients with heart failure are consistent with the findings of experimental studies that have shown that the effects of SGLT2 inhibitors are likely related to actions on the heart and kidney to promote autophagic flux, nutrient deprivation signalling and transmembrane sodium transport.

Transcriptomic characterization of culture-associated changes in murine and human precision-cut tissue slices.

Our knowledge of complex pathological mechanisms underlying organ fibrosis is predominantly derived from animal studies. However, relevance of animal models for human disease is limited; therefore, an ex vivo model of human precision-cut tissue slices (PCTS) might become an indispensable tool in fibrosis research and drug development by bridging the animal-human translational gap. This study, presented as two parts, provides comprehensive characterization of the dynamic transcriptional changes in PCTS during culture by RNA sequencing. Part I investigates the differences in culture-induced responses in murine and human PCTS derived from healthy liver, kidney and gut. Part II delineates the molecular processes in cultured human PCTS generated from diseased liver, kidney and ileum. We demonstrated that culture was associated with extensive transcriptional changes and impacted PCTS in a universal way across the organs and two species by triggering an inflammatory response and fibrosis-related extracellular matrix (ECM) remodelling. All PCTS shared mRNA upregulation of IL-11 and ECM-degrading enzymes MMP3 and MMP10. Slice preparation and culturing activated numerous pathways across all PCTS, especially those involved in inflammation (IL-6, IL-8 and HMGB1 signalling) and tissue remodelling (osteoarthritis pathway and integrin signalling). Despite the converging effects of culture, PCTS display species-, organ- and pathology-specific differences in the regulation of genes and canonical pathways. The underlying pathology in human diseased PCTS endures and influences biological processes like cytokine release. Our study reinforces the use of PCTS as an ex vivo fibrosis model and supports future studies towards its validation as a preclinical tool for drug development.

How Phosphorylation and ATPase Activity Regulate Anion Flux though the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR).

The cystic fibrosis transmembrane conductance regulator (CFTR, ABCC7), mutations of which cause cystic fibrosis, belongs to the ATP-binding cassette (ABC) transporter family and works as a channel for small anions, such as chloride and bicarbonate. Anion channel activity is known to depend on phosphorylation by cAMP-dependent protein kinase A (PKA) and CFTR-ATPase activity. Whereas anion channel activity has been extensively investigated, phosphorylation and CFTR-ATPase activity are still poorly understood. Here, we show that the two processes can be measured in a label-free and non-invasive manner in real time in live cells, stably transfected with CFTR. This study reveals three key findings. (i) The major contribution (≥90%) to the total CFTR-related ATP hydrolysis rate is due to phosphorylation by PKA and the minor contribution (≤10%) to CFTR-ATPase activity. (ii) The mutant CFTR-E1371S that is still conductive, but defective in ATP hydrolysis, is not phosphorylated, suggesting that phosphorylation requires a functional nucleotide binding domain and occurs in the post-hydrolysis transition state. (iii) CFTR-ATPase activity is inversely related to CFTR anion flux. The present data are consistent with a model in which CFTR is in a closed conformation with two ATPs bound. The open conformation is induced by ATP hydrolysis and corresponds to the post-hydrolysis transition state that is stabilized by phosphorylation and binding of chloride channel potentiators.

Automated curation of gene name normalization results using the Konstanz information miner.

BACKGROUND: Gene name recognition and normalization is, together with detection of other named entities, a crucial step in biomedical text mining and the underlying basis for development of more advanced techniques like extraction of complex events. While the current state of the art solutions achieve highly promising results on average, performance can drop significantly for specific genes with highly ambiguous synonyms. Depending on the topic of interest, this can cause the need for extensive manual curation of such text mining results. Our goal was to enhance this curation step based on tools widely used in pharmaceutical industry utilizing the text processing and classification capabilities of the Konstanz Information Miner (KNIME) along with publicly available sources. RESULTS: F-score achieved on gene specific test corpora for highly ambiguous genes could be improved from values close to zero, due to very low precision, to values >0.9 for several cases. Interestingly the presented approach even resulted in an increased F-score for genes showing already good results in initial gene name normalization. For most test cases, we could significantly improve precision, while retaining a high recall. CONCLUSIONS: We could show that KNIME can be used to assist in manual curation of text mining results containing high numbers of false positive hits. Our results also indicate that it could be beneficial for future development in the field of gene name normalization to create gene specific training corpora based on incorrectly identified genes common to current state of the art algorithms.

Dataset of the frequency patterns of publications annotated to human protein-coding genes, their protein products and genetic relevance.

We present data concerning the distribution of scientific publications for human protein-coding genes together with their protein products and genetic relevance. We annotated the gene2pubmed dataset Maglott et al., 2007 provided by the NCBI (National Center for Biotechnology Information) with publication years, genetic metadata corresponding to Online Mendelian Inheritance in Man (OMIM) Hamosh et al., 2005 entries and the frequency of their appearance in Genome-Wide Association Studies (GWAS) Buniello et al., 2019 provided by the European Bioinformatics Institute (EBI) using the KNIME® Analytics Platform Berthold et al., 2008. The results of this data integration process comprise two datasets: 1) A dataset containing information on all human protein-coding genes that can be used to analyse the number of scientific publications in context of the potential disease relevance of the individual genes. 2) A table with the annual and cumulated number of PubMed entries. For further interpretation of the data presented in this article, please see the research article 'Target 2035 - probing the human proteome' by Carter et al. https://doi.org/10.1016/j.drudis.2019.06.020 Carter et al., 2019.

Target 2035: probing the human proteome.

Biomedical scientists tend to focus on only a small fraction of the proteins encoded by the human genome despite overwhelming genetic evidence that many understudied proteins are important for human disease. One of the best ways to interrogate the function of a protein and to determine its relevance as a drug target is by using a pharmacological modulator, such as a chemical probe or an antibody. If these tools were available for most human proteins, it should be possible to translate the tremendous advances in genomics into a greater understanding of human health and disease, and catalyze the creation of innovative new medicines. Target 2035 is a global federation for developing and applying new technologies with the goal of creating chemogenomic libraries, chemical probes, and/or functional antibodies for the entire proteome.

Exploiting orthology and de novo transcriptome assembly to refine target sequence information.

BACKGROUND: The ability to generate recombinant drug target proteins is important for drug discovery research as it facilitates the investigation of drug-target-interactions in vitro. To accomplish this, the target's exact protein sequence is required. Public databases, such as Ensembl, UniProt and RefSeq, are extensive protein and nucleotide sequence repositories. However, many sequences for non-human organisms are predicted by computational pipelines and may thus be incomplete or incorrect. This could lead to misinterpreted experimental outcomes due to gaps or errors in orthologous drug target sequences. Transcriptome analysis by RNA-Seq has been established as a standard method for gene expression analysis. Apart from this common application, paired-end RNA-Seq data can also be used to obtain full coverage cDNA sequences via de novo transcriptome assembly. METHODS: To assess whether de novo transcriptome assemblies can be used to determine a protein's sequence by searching the assembly for a known orthologous sequence, we generated 3 × 6 = 18 tissue specific assemblies (three organs: brain, kidney and liver; six species: human, mouse, rat, dog, pig and cynomolgus monkey). These assemblies and the manually curated human protein sequences from UniProtKB/Swiss-Prot were used in a reciprocal BLAST search to identify best matching hits. We automated and generalised our approach and present the a&o-tool, a workflow which exploits de novo assemblies of paired-end RNA-Seq data and orthology information for target sequence validation and refinement across related species. Furthermore, the a&o-tool extracts best hits' sequences from a reciprocal BLAST search, translates them into protein sequences, computes a multiple sequence alignment and quantifies the refinement. RESULTS: For the three human assemblies we observed a hit rate greater than 60% with 100% sequence coverage and identity. For assemblies from the other species we observed similar hit rates and coverage with highest identities for cynomolgus monkey. CONCLUSIONS: In summary, we show how to refine protein sequences using RNA-Seq data and sequence information from closely related species. With the a&o-tool we provide a fully automated pipeline to perform refinement including cDNA translation and multiple sequence alignment for visual inspection. The major prerequisite for applying the a&o-tool is high quality sequencing data.

H2r: identification of evolutionary important residues by means of an entropy based analysis of multiple sequence alignments.

BACKGROUND: A multiple sequence alignment (MSA) generated for a protein can be used to characterise residues by means of a statistical analysis of single columns. In addition to the examination of individual positions, the investigation of co-variation of amino acid frequencies offers insights into function and evolution of the protein and residues. RESULTS: We introduce conn(k), a novel parameter for the characterisation of individual residues. For each residue k, conn(k) is the number of most extreme signals of co-evolution. These signals were deduced from a normalised mutual information (MI) value U(k, l) computed for all pairs of residues k, l. We demonstrate that conn(k) is a more robust indicator than an individual MI-value for the prediction of residues most plausibly important for the evolution of a protein. This proposition was inferred by means of statistical methods. It was further confirmed by the analysis of several proteins. A server, which computes conn(k)-values is available at http://www-bioinf.uni-regensburg.de. CONCLUSION: The algorithms H2r, which analyses MSAs and computes conn(k)-values, characterises a specific class of residues. In contrast to strictly conserved ones, these residues possess some flexibility in the composition of side chains. However, their allocation is sensibly balanced with several other positions, as indicated by conn(k).